From Google’s Self Driving Car to Apple’s Siri, all of these are applications of Deep Learning.
And that is why top companies such as Google, Facebook and Amazon are looking to hire people skilled in the domain of deep learning.
So, keeping in mind, the importance of deep learning, we have come up with this full course on ‘Deep Learning’.
The session will cover the following topics:
2:11 - Installing Python and other IDEs
6:15 - Variables in Python
8:49 - Data Types in Python
11:36 - Operators in Python
17:29 - Python Strings
21:50 - Data Structures in Python
33:32 - Flow Control Statements in Python
51:15 - Python Functions
55:41 - Object-Oriented Programming in Python
1:10:23 - Numpy in Python
1:29:05 - Pandas in Python
1:52:02 - Matplotlib in Python
2:16:03 - History behind Deep Neural Networks
2:22:05 - Relationship between Artificial and Biological Neuron
2:32:11 - Working of Perceptron
3:11:45 - Demo on McCullohPitt and Rosenblat Neurons
3:31:43 - Architecture of Artificial Neural Network
4:34:15 - Activation Functions
5:40:56 - Demo on Wine Dataset using Keras Sequential Model
8:11:14 - Back-propagation and Gradient Descent Algorithm
8:49:42 - Demo on MNIST dataset
And that is why top companies such as Google, Facebook and Amazon are looking to hire people skilled in the domain of deep learning.
So, keeping in mind, the importance of deep learning, we have come up with this full course on ‘Deep Learning’.
The session will cover the following topics:
2:11 - Installing Python and other IDEs
6:15 - Variables in Python
8:49 - Data Types in Python
11:36 - Operators in Python
17:29 - Python Strings
21:50 - Data Structures in Python
33:32 - Flow Control Statements in Python
51:15 - Python Functions
55:41 - Object-Oriented Programming in Python
1:10:23 - Numpy in Python
1:29:05 - Pandas in Python
1:52:02 - Matplotlib in Python
2:16:03 - History behind Deep Neural Networks
2:22:05 - Relationship between Artificial and Biological Neuron
2:32:11 - Working of Perceptron
3:11:45 - Demo on McCullohPitt and Rosenblat Neurons
3:31:43 - Architecture of Artificial Neural Network
4:34:15 - Activation Functions
5:40:56 - Demo on Wine Dataset using Keras Sequential Model
8:11:14 - Back-propagation and Gradient Descent Algorithm
8:49:42 - Demo on MNIST dataset
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